Conventional machine maintenance adapts corrective maintenance in which a machine is repaired after it breaks down, or uniform preventive maintenance which is performed at predetermined intervals. Corrective maintenance entails a lot of time and cost for repair. Preventive maintenance generates unnecessary part and oil waste due to its uniformity and thereby imposes greater costs on customers. Further preventive maintenance is expensive because of the intensive labor required. There is a requirement for a departure from such conventional maintenance manners and for conversion to predictive maintenance in the future.
In predictive maintenance, the degree of soundness is diagnosed by understanding data of load and environment during operation, a database of past maintenance history, physical failure and others, and further deterioration and remaining life are predicted in order to find a defect on a machine at an early stage and to provide a safe operation environment.
For example, patent reference 1 relates to an abnormality diagnosis apparatus for a working vehicle such as a construction machine; a pressure sensor for detecting discharge pressure from a hydraulic pump, an engine speed sensor for detecting engine speed, an oil temperature sensor for detecting the oil temperature in a hydraulic circuit and a communication device for radio transmitting detection data by these sensors to a network management center (a network station) are installed in a vehicle body of a working machine (a hydraulic excavator), and a monitoring station (e.g., an office of the manager of the working machine) obtains the detection data of the working machine from the network management station through the Internet and diagnoses any abnormalities of the working machine based on the detection data.
Further, patent reference 2 relates to an abnormality detection apparatus for a fixed machinery facility such as a batch plant or a continuous plant; normal data when the object plant is in a normal state is previously collected, on the basis of the normal data, characteristics of the normal data are extracted using a Self-Organizing Map; on the basis of the characteristics, a characteristic map indicating distance relationships between outputting units is created and stored as a normal state model, and an abnormality of the object plant is detected based on the normal state model and input data (input vectors). Here, the normal state model is formed by converting multi-dimensional data into a visualized two-dimensional map as shown in FIG. 20 (in which the multi-dimensional data is classified into five clusters expressed by regions with symbols R1-R5), and if input data has a characteristic identical to the normal state model, the input data is judged to be normal data. The technique of patent reference 2 can totally detect an abnormality of multi-dimensional input data in real time.
Patent reference 1: Japanese Patent Application Laid-Open (KOKAI) Publication No. 2002-323013
Patent reference 2: Japanese Patent Application Laid-Open (KOKAI) Publication No. HEI 11-338848